Assumption Checks
Import model objects
load('/Users/hp2500/Google Drive/STUDY/Columbia/Research/Corona/Data/GER/ger_list_results_fixed_window.RData')
load('/Users/hp2500/Google Drive/STUDY/Columbia/Research/Corona/Data/US/us_list_results_fixed_window.RData')Define Functions
list_iterater <- function(models, test) {
for(i in models){
for(j in i){
if(test == 'qq'){j %>% plot(2)}
if(test == 'ks'){j %>% resid() %>% ks.test(y=pnorm) %>% print()}
if(test == 'bp'){j %>% bptest() %>% print()}
if(test == 'ph'){j %>% cox.zph() %>% print()}
}
}
}
Assumptions GER COVID-19 onsets (proportional hazards)
list_iterater(ger_list_results$ger_cox_prev_onset, test = 'ph') chisq df p
pers 6.21 1 0.013
GLOBAL 6.21 1 0.013
chisq df p
pers 4.007 1 0.045
age 21.844 1 3.0e-06
male 0.454 1 0.500
conservative 15.769 1 7.2e-05
GLOBAL 25.231 4 4.5e-05
chisq df p
pers 1.30051 1 0.25
academics 0.10832 1 0.74
medinc 0.00668 1 0.93
manufact 0.07463 1 0.78
GLOBAL 1.38751 4 0.85
chisq df p
pers 6.89 1 0.0087
airport_dist 7.94 1 0.0048
tourism 1.65 1 0.1994
healthcare 6.31 1 0.0120
popdens 6.31 1 0.0120
GLOBAL 17.20 5 0.0041
chisq df p
pers 1.05049 1 0.30539
age 11.81505 1 0.00059
male 4.76514 1 0.02904
conservative 4.75085 1 0.02928
academics 0.11884 1 0.73030
medinc 2.27744 1 0.13127
manufact 0.49479 1 0.48180
airport_dist 0.69603 1 0.40412
tourism 0.07452 1 0.78486
healthcare 0.00138 1 0.97032
popdens 1.36622 1 0.24246
GLOBAL 17.21722 11 0.10161
chisq df p
pers 2.51 1 0.11
GLOBAL 2.51 1 0.11
chisq df p
pers 7.888 1 0.00498
age 19.642 1 9.3e-06
male 0.658 1 0.41721
conservative 14.163 1 0.00017
GLOBAL 23.934 4 8.2e-05
chisq df p
pers 1.6479 1 0.20
academics 0.0923 1 0.76
medinc 0.0737 1 0.79
manufact 0.0601 1 0.81
GLOBAL 1.9272 4 0.75
chisq df p
pers 3.23 1 0.0723
airport_dist 8.82 1 0.0030
tourism 1.55 1 0.2126
healthcare 6.01 1 0.0142
popdens 6.41 1 0.0114
GLOBAL 16.26 5 0.0061
chisq df p
pers 3.45e+00 1 0.06333
age 1.28e+01 1 0.00034
male 4.89e+00 1 0.02701
conservative 5.00e+00 1 0.02530
academics 8.68e-02 1 0.76827
medinc 2.30e+00 1 0.12979
manufact 6.41e-01 1 0.42342
airport_dist 8.22e-01 1 0.36449
tourism 6.81e-02 1 0.79418
healthcare 9.25e-04 1 0.97574
popdens 1.38e+00 1 0.23943
GLOBAL 1.87e+01 11 0.06668
chisq df p
pers 10.2 1 0.0014
GLOBAL 10.2 1 0.0014
chisq df p
pers 9.075 1 0.0026
age 20.874 1 4.9e-06
male 0.256 1 0.6128
conservative 15.413 1 8.6e-05
GLOBAL 29.120 4 7.4e-06
chisq df p
pers 3.3334 1 0.068
academics 0.3435 1 0.558
medinc 0.0519 1 0.820
manufact 0.0950 1 0.758
GLOBAL 3.3556 4 0.500
chisq df p
pers 8.30 1 0.0040
airport_dist 7.50 1 0.0062
tourism 1.70 1 0.1924
healthcare 7.55 1 0.0060
popdens 6.36 1 0.0117
GLOBAL 21.03 5 0.0008
chisq df p
pers 4.09e+00 1 0.04323
age 1.17e+01 1 0.00064
male 4.38e+00 1 0.03633
conservative 4.86e+00 1 0.02754
academics 2.09e-01 1 0.64747
medinc 2.49e+00 1 0.11445
manufact 4.16e-01 1 0.51913
airport_dist 7.69e-01 1 0.38049
tourism 8.12e-02 1 0.77565
healthcare 5.23e-04 1 0.98176
popdens 1.31e+00 1 0.25286
GLOBAL 1.83e+01 11 0.07556
chisq df p
pers 7.14 1 0.0076
GLOBAL 7.14 1 0.0076
chisq df p
pers 3.46 1 0.0628
age 21.13 1 4.3e-06
male 0.44 1 0.5072
conservative 15.05 1 0.0001
GLOBAL 28.94 4 8.1e-06
chisq df p
pers 2.9859 1 0.084
academics 0.0492 1 0.825
medinc 0.0130 1 0.909
manufact 0.0662 1 0.797
GLOBAL 3.1061 4 0.540
chisq df p
pers 7.98 1 0.00474
airport_dist 8.47 1 0.00362
tourism 1.55 1 0.21352
healthcare 5.87 1 0.01537
popdens 6.24 1 0.01247
GLOBAL 20.74 5 0.00091
chisq df p
pers 2.34952 1 0.1253
age 12.10476 1 0.0005
male 5.08073 1 0.0242
conservative 4.84600 1 0.0277
academics 0.03360 1 0.8546
medinc 2.16525 1 0.1412
manufact 0.63890 1 0.4241
airport_dist 0.64944 1 0.4203
tourism 0.06193 1 0.8035
healthcare 0.00924 1 0.9234
popdens 1.17671 1 0.2780
GLOBAL 21.93744 11 0.0249
chisq df p
pers 5.91 1 0.015
GLOBAL 5.91 1 0.015
chisq df p
pers 4.570 1 0.033
age 19.880 1 8.2e-06
male 0.332 1 0.564
conservative 15.516 1 8.2e-05
GLOBAL 26.962 4 2.0e-05
chisq df p
pers 0.507 1 0.48
academics 0.202 1 0.65
medinc 0.128 1 0.72
manufact 0.280 1 0.60
GLOBAL 1.003 4 0.91
chisq df p
pers 5.90 1 0.01512
airport_dist 7.85 1 0.00509
tourism 1.40 1 0.23743
healthcare 5.39 1 0.02023
popdens 7.23 1 0.00716
GLOBAL 20.61 5 0.00096
chisq df p
pers 0.7484 1 0.38699
age 10.8876 1 0.00097
male 4.0270 1 0.04478
conservative 5.2602 1 0.02182
academics 0.2251 1 0.63517
medinc 1.1519 1 0.28314
manufact 0.1375 1 0.71080
airport_dist 0.8884 1 0.34590
tourism 0.0349 1 0.85181
healthcare 0.0493 1 0.82426
popdens 1.4904 1 0.22216
GLOBAL 15.5926 11 0.15694Assumptions US COVID-19 onsets (proportional hazards)
list_iterater(us_list_results$us_cox_prev_onset, test = 'ph') chisq df p
pers 133 1 <2e-16
GLOBAL 133 1 <2e-16
chisq df p
pers 106.580 1 < 2e-16
age 0.453 1 0.5
male 21.391 1 3.7e-06
conservative 102.647 1 < 2e-16
GLOBAL 172.622 4 < 2e-16
chisq df p
pers 96.8 1 < 2e-16
academics 144.2 1 < 2e-16
medinc 47.3 1 5.9e-12
manufact 61.3 1 5.0e-15
GLOBAL 182.7 4 < 2e-16
chisq df p
pers 92.07 1 < 2e-16
airport_dist 8.83 1 0.003
tourism 15.46 1 8.4e-05
healthcare 37.50 1 9.1e-10
popdens 3.87 1 0.049
GLOBAL 122.81 5 < 2e-16
chisq df p
pers 71.8062 1 < 2e-16
age 0.0164 1 0.89806
male 21.4651 1 3.6e-06
conservative 70.0537 1 < 2e-16
academics 110.6622 1 < 2e-16
medinc 32.3876 1 1.3e-08
manufact 46.1464 1 1.1e-11
airport_dist 2.0257 1 0.15466
tourism 13.2975 1 0.00027
healthcare 25.6451 1 4.1e-07
popdens 32.9347 1 9.5e-09
GLOBAL 173.6175 11 < 2e-16
chisq df p
pers 0.729 1 0.39
GLOBAL 0.729 1 0.39
chisq df p
pers 2.56 1 0.11
age 1.30 1 0.25
male 20.65 1 5.5e-06
conservative 107.90 1 < 2e-16
GLOBAL 124.50 4 < 2e-16
chisq df p
pers 0.112 1 0.74
academics 153.781 1 < 2e-16
medinc 53.480 1 2.6e-13
manufact 64.290 1 1.1e-15
GLOBAL 170.151 4 < 2e-16
chisq df p
pers 0.425 1 0.5143
airport_dist 7.913 1 0.0049
tourism 15.657 1 7.6e-05
healthcare 41.027 1 1.5e-10
popdens 1.702 1 0.1921
GLOBAL 59.516 5 1.5e-11
chisq df p
pers 2.14e-01 1 0.64358
age 4.78e-03 1 0.94491
male 1.97e+01 1 9.2e-06
conservative 7.15e+01 1 < 2e-16
academics 1.17e+02 1 < 2e-16
medinc 3.90e+01 1 4.2e-10
manufact 4.88e+01 1 2.8e-12
airport_dist 1.27e+00 1 0.26002
tourism 1.39e+01 1 0.00019
healthcare 2.87e+01 1 8.3e-08
popdens 3.04e+01 1 3.5e-08
GLOBAL 1.71e+02 11 < 2e-16
chisq df p
pers 1.91 1 0.17
GLOBAL 1.91 1 0.17
chisq df p
pers 0.913 1 0.34
age 1.251 1 0.26
male 22.838 1 1.8e-06
conservative 111.394 1 < 2e-16
GLOBAL 124.947 4 < 2e-16
chisq df p
pers 0.964 1 0.33
academics 148.095 1 < 2e-16
medinc 51.959 1 5.7e-13
manufact 62.359 1 2.9e-15
GLOBAL 162.064 4 < 2e-16
chisq df p
pers 0.831 1 0.3619
airport_dist 8.887 1 0.0029
tourism 13.795 1 0.0002
healthcare 39.805 1 2.8e-10
popdens 3.149 1 0.0760
GLOBAL 58.645 5 2.3e-11
chisq df p
pers 2.52e-01 1 0.61546
age 4.88e-03 1 0.94429
male 2.27e+01 1 1.9e-06
conservative 7.45e+01 1 < 2e-16
academics 1.14e+02 1 < 2e-16
medinc 3.65e+01 1 1.6e-09
manufact 4.77e+01 1 4.9e-12
airport_dist 1.94e+00 1 0.16421
tourism 1.26e+01 1 0.00038
healthcare 2.73e+01 1 1.7e-07
popdens 3.49e+01 1 3.6e-09
GLOBAL 1.69e+02 11 < 2e-16
chisq df p
pers 0.402 1 0.53
GLOBAL 0.402 1 0.53
chisq df p
pers 1.03 1 0.31
age 1.37 1 0.24
male 21.87 1 2.9e-06
conservative 111.50 1 < 2e-16
GLOBAL 134.61 4 < 2e-16
chisq df p
pers 0.603 1 0.44
academics 153.090 1 < 2e-16
medinc 54.952 1 1.2e-13
manufact 68.508 1 < 2e-16
GLOBAL 172.867 4 < 2e-16
chisq df p
pers 0.0332 1 0.8554
airport_dist 8.7075 1 0.0032
tourism 15.3470 1 8.9e-05
healthcare 42.5992 1 6.7e-11
popdens 3.5426 1 0.0598
GLOBAL 61.8373 5 5.1e-12
chisq df p
pers 3.81e-03 1 0.95076
age 1.66e-04 1 0.98973
male 2.11e+01 1 4.4e-06
conservative 7.67e+01 1 < 2e-16
academics 1.19e+02 1 < 2e-16
medinc 3.83e+01 1 6.1e-10
manufact 5.16e+01 1 6.7e-13
airport_dist 1.77e+00 1 0.18394
tourism 1.36e+01 1 0.00023
healthcare 3.01e+01 1 4.2e-08
popdens 3.77e+01 1 8.1e-10
GLOBAL 1.76e+02 11 < 2e-16
chisq df p
pers 49.7 1 1.8e-12
GLOBAL 49.7 1 1.8e-12
chisq df p
pers 35.610 1 2.4e-09
age 0.744 1 0.39
male 21.775 1 3.1e-06
conservative 109.483 1 < 2e-16
GLOBAL 133.700 4 < 2e-16
chisq df p
pers 47.0 1 7.2e-12
academics 158.3 1 < 2e-16
medinc 58.3 1 2.2e-14
manufact 62.3 1 2.9e-15
GLOBAL 177.2 4 < 2e-16
chisq df p
pers 33.48 1 7.2e-09
airport_dist 6.46 1 0.01102
tourism 12.01 1 0.00053
healthcare 41.31 1 1.3e-10
popdens 3.34 1 0.06744
GLOBAL 81.47 5 4.1e-16
chisq df p
pers 25.1056 1 5.4e-07
age 0.0324 1 0.85712
male 21.4504 1 3.6e-06
conservative 71.5756 1 < 2e-16
academics 119.2835 1 < 2e-16
medinc 40.8184 1 1.7e-10
manufact 47.3892 1 5.8e-12
airport_dist 1.6077 1 0.20481
tourism 11.5992 1 0.00066
healthcare 28.6560 1 8.6e-08
popdens 30.7441 1 2.9e-08
GLOBAL 176.2965 11 < 2e-16Assumptions GER COVID-19 growth rates (normality of residuals)
list_iterater(ger_list_results$ger_lm_prev_slope, test = 'qq')
list_iterater(ger_list_results$ger_lm_prev_slope, test = 'bp')
studentized Breusch-Pagan test
data: .
BP = 0.20014, df = 1, p-value = 0.6546
studentized Breusch-Pagan test
data: .
BP = 1.7565, df = 4, p-value = 0.7804
studentized Breusch-Pagan test
data: .
BP = 8.8926, df = 4, p-value = 0.06384
studentized Breusch-Pagan test
data: .
BP = 12.765, df = 5, p-value = 0.02568
studentized Breusch-Pagan test
data: .
BP = 19.536, df = 11, p-value = 0.05213
studentized Breusch-Pagan test
data: .
BP = 0.014025, df = 1, p-value = 0.9057
studentized Breusch-Pagan test
data: .
BP = 2.0882, df = 4, p-value = 0.7195
studentized Breusch-Pagan test
data: .
BP = 8.2989, df = 4, p-value = 0.08122
studentized Breusch-Pagan test
data: .
BP = 10.635, df = 5, p-value = 0.05912
studentized Breusch-Pagan test
data: .
BP = 18.827, df = 11, p-value = 0.06426
studentized Breusch-Pagan test
data: .
BP = 0.9348, df = 1, p-value = 0.3336
studentized Breusch-Pagan test
data: .
BP = 2.8075, df = 4, p-value = 0.5905
studentized Breusch-Pagan test
data: .
BP = 8.2707, df = 4, p-value = 0.08215
studentized Breusch-Pagan test
data: .
BP = 10.975, df = 5, p-value = 0.05188
studentized Breusch-Pagan test
data: .
BP = 18.978, df = 11, p-value = 0.06149
studentized Breusch-Pagan test
data: .
BP = 1.9621, df = 1, p-value = 0.1613
studentized Breusch-Pagan test
data: .
BP = 2.3503, df = 4, p-value = 0.6716
studentized Breusch-Pagan test
data: .
BP = 8.6682, df = 4, p-value = 0.06995
studentized Breusch-Pagan test
data: .
BP = 17.72, df = 5, p-value = 0.003318
studentized Breusch-Pagan test
data: .
BP = 19.885, df = 11, p-value = 0.04694
studentized Breusch-Pagan test
data: .
BP = 0.77295, df = 1, p-value = 0.3793
studentized Breusch-Pagan test
data: .
BP = 2.6459, df = 4, p-value = 0.6187
studentized Breusch-Pagan test
data: .
BP = 9.1115, df = 4, p-value = 0.05837
studentized Breusch-Pagan test
data: .
BP = 11.656, df = 5, p-value = 0.03981
studentized Breusch-Pagan test
data: .
BP = 19.53, df = 11, p-value = 0.05222Assumptions US COVID-19 growth rates (normality of residuals)
list_iterater(us_list_results$us_lm_prev_slope, test = 'qq')
list_iterater(us_list_results$us_lm_prev_slope, test = 'bp')
studentized Breusch-Pagan test
data: .
BP = 25.881, df = 1, p-value = 3.63e-07
studentized Breusch-Pagan test
data: .
BP = 58.996, df = 4, p-value = 4.715e-12
studentized Breusch-Pagan test
data: .
BP = 27.503, df = 4, p-value = 1.573e-05
studentized Breusch-Pagan test
data: .
BP = 88.757, df = 5, p-value < 2.2e-16
studentized Breusch-Pagan test
data: .
BP = 121.41, df = 11, p-value < 2.2e-16
studentized Breusch-Pagan test
data: .
BP = 0.45532, df = 1, p-value = 0.4998
studentized Breusch-Pagan test
data: .
BP = 58.951, df = 4, p-value = 4.819e-12
studentized Breusch-Pagan test
data: .
BP = 35.857, df = 4, p-value = 3.096e-07
studentized Breusch-Pagan test
data: .
BP = 93.365, df = 5, p-value < 2.2e-16
studentized Breusch-Pagan test
data: .
BP = 127.65, df = 11, p-value < 2.2e-16
studentized Breusch-Pagan test
data: .
BP = 3.7171, df = 1, p-value = 0.05386
studentized Breusch-Pagan test
data: .
BP = 59.824, df = 4, p-value = 3.158e-12
studentized Breusch-Pagan test
data: .
BP = 21.132, df = 4, p-value = 0.0002982
studentized Breusch-Pagan test
data: .
BP = 84.501, df = 5, p-value < 2.2e-16
studentized Breusch-Pagan test
data: .
BP = 119.57, df = 11, p-value < 2.2e-16
studentized Breusch-Pagan test
data: .
BP = 7.0583, df = 1, p-value = 0.00789
studentized Breusch-Pagan test
data: .
BP = 65.59, df = 4, p-value = 1.933e-13
studentized Breusch-Pagan test
data: .
BP = 57.311, df = 4, p-value = 1.064e-11
studentized Breusch-Pagan test
data: .
BP = 108.23, df = 5, p-value < 2.2e-16
studentized Breusch-Pagan test
data: .
BP = 147.26, df = 11, p-value < 2.2e-16
studentized Breusch-Pagan test
data: .
BP = 12.544, df = 1, p-value = 0.0003975
studentized Breusch-Pagan test
data: .
BP = 58.66, df = 4, p-value = 5.545e-12
studentized Breusch-Pagan test
data: .
BP = 31.917, df = 4, p-value = 1.989e-06
studentized Breusch-Pagan test
data: .
BP = 94.189, df = 5, p-value < 2.2e-16
studentized Breusch-Pagan test
data: .
BP = 123.74, df = 11, p-value < 2.2e-16Assumptions GER socdist onsets
list_iterater(ger_list_results$ger_cox_socdist_cpt, test = 'ph') chisq df p
pers 0.187 1 0.67
GLOBAL 0.187 1 0.67
chisq df p
pers 0.589 1 0.443
age 1.510 1 0.219
male 5.639 1 0.018
conservative 1.333 1 0.248
GLOBAL 8.287 4 0.082
chisq df p
pers 0.07128 1 0.789
academics 0.00457 1 0.946
medinc 6.30499 1 0.012
manufact 5.40654 1 0.020
GLOBAL 9.07479 4 0.059
chisq df p
pers 0.0093 1 0.923
airport_dist 3.7655 1 0.052
tourism 0.4039 1 0.525
healthcare 0.9540 1 0.329
popdens 0.0948 1 0.758
GLOBAL 5.0000 5 0.416
chisq df p
pers 0.0439 1 0.834
age 3.8044 1 0.051
male 6.2038 1 0.013
conservative 2.3823 1 0.123
academics 0.0481 1 0.826
medinc 3.3040 1 0.069
manufact 2.3737 1 0.123
airport_dist 6.1123 1 0.013
tourism 1.5087 1 0.219
healthcare 1.5767 1 0.209
popdens 0.0263 1 0.871
onset_prev 2.8203 1 0.093
slope_prev 4.1380 1 0.042
GLOBAL 17.3136 13 0.185
chisq df p
pers 0.284 1 0.59
GLOBAL 0.284 1 0.59
chisq df p
pers 1.15 1 0.284
age 1.34 1 0.247
male 5.15 1 0.023
conservative 1.12 1 0.290
GLOBAL 7.65 4 0.105
chisq df p
pers 0.5249 1 0.469
academics 0.0037 1 0.952
medinc 5.4471 1 0.020
manufact 5.4390 1 0.020
GLOBAL 7.8952 4 0.095
chisq df p
pers 0.1079 1 0.743
airport_dist 4.6196 1 0.032
tourism 0.4192 1 0.517
healthcare 1.2591 1 0.262
popdens 0.0509 1 0.822
GLOBAL 6.7462 5 0.240
chisq df p
pers 0.5092 1 0.4755
age 3.8442 1 0.0499
male 5.7297 1 0.0167
conservative 2.3451 1 0.1257
academics 0.0958 1 0.7570
medinc 3.2215 1 0.0727
manufact 2.2607 1 0.1327
airport_dist 6.8590 1 0.0088
tourism 1.4968 1 0.2212
healthcare 1.6372 1 0.2007
popdens 0.0114 1 0.9150
onset_prev 3.0881 1 0.0789
slope_prev 4.5326 1 0.0333
GLOBAL 17.6092 13 0.1729
chisq df p
pers 1.04 1 0.31
GLOBAL 1.04 1 0.31
chisq df p
pers 1.52 1 0.218
age 1.18 1 0.276
male 4.82 1 0.028
conservative 1.05 1 0.307
GLOBAL 7.57 4 0.109
chisq df p
pers 0.9332 1 0.334
academics 0.0254 1 0.873
medinc 5.5409 1 0.019
manufact 4.7740 1 0.029
GLOBAL 8.6942 4 0.069
chisq df p
pers 1.232 1 0.27
airport_dist 4.230 1 0.04
tourism 0.475 1 0.49
healthcare 1.037 1 0.31
popdens 0.103 1 0.75
GLOBAL 5.784 5 0.33
chisq df p
pers 0.8818 1 0.3477
age 3.7021 1 0.0543
male 5.4792 1 0.0192
conservative 2.2946 1 0.1298
academics 0.1120 1 0.7379
medinc 2.5620 1 0.1095
manufact 1.7891 1 0.1810
airport_dist 7.1031 1 0.0077
tourism 1.7503 1 0.1858
healthcare 1.6702 1 0.1962
popdens 0.0123 1 0.9115
onset_prev 2.9767 1 0.0845
slope_prev 4.3407 1 0.0372
GLOBAL 17.9668 13 0.1588
chisq df p
pers 0.667 1 0.41
GLOBAL 0.667 1 0.41
chisq df p
pers 1.50 1 0.221
age 1.30 1 0.255
male 4.96 1 0.026
conservative 1.09 1 0.295
GLOBAL 8.17 4 0.086
chisq df p
pers 0.96909 1 0.325
academics 0.00695 1 0.934
medinc 5.51116 1 0.019
manufact 5.08746 1 0.024
GLOBAL 7.82380 4 0.098
chisq df p
pers 0.4477 1 0.503
airport_dist 4.4029 1 0.036
tourism 0.4347 1 0.510
healthcare 1.0666 1 0.302
popdens 0.0851 1 0.770
GLOBAL 5.8358 5 0.323
chisq df p
pers 1.3945 1 0.2377
age 3.7995 1 0.0513
male 5.6669 1 0.0173
conservative 2.3341 1 0.1266
academics 0.0935 1 0.7598
medinc 3.2418 1 0.0718
manufact 2.2310 1 0.1353
airport_dist 6.8871 1 0.0087
tourism 1.5300 1 0.2161
healthcare 1.6230 1 0.2027
popdens 0.0108 1 0.9173
onset_prev 2.9882 1 0.0839
slope_prev 4.5077 1 0.0337
GLOBAL 17.5141 13 0.1769
chisq df p
pers 0.66 1 0.42
GLOBAL 0.66 1 0.42
chisq df p
pers 0.268 1 0.605
age 1.388 1 0.239
male 4.860 1 0.027
conservative 1.229 1 0.268
GLOBAL 6.221 4 0.183
chisq df p
pers 0.91068 1 0.340
academics 0.00965 1 0.922
medinc 5.97668 1 0.014
manufact 5.24467 1 0.022
GLOBAL 7.79339 4 0.099
chisq df p
pers 0.6571 1 0.418
airport_dist 4.0898 1 0.043
tourism 0.4325 1 0.511
healthcare 1.0549 1 0.304
popdens 0.0785 1 0.779
GLOBAL 5.3354 5 0.376
chisq df p
pers 0.5507 1 0.4580
age 3.8479 1 0.0498
male 5.6687 1 0.0173
conservative 2.3884 1 0.1222
academics 0.0979 1 0.7544
medinc 3.2705 1 0.0705
manufact 2.2318 1 0.1352
airport_dist 6.7326 1 0.0095
tourism 1.5199 1 0.2176
healthcare 1.5803 1 0.2087
popdens 0.0102 1 0.9194
onset_prev 3.0841 1 0.0791
slope_prev 4.5356 1 0.0332
GLOBAL 18.3991 13 0.1429Assumptions US socdist onsets
list_iterater(us_list_results$us_cox_socdist_cpt, test = 'ph') chisq df p
pers 40.1 1 2.4e-10
GLOBAL 40.1 1 2.4e-10
chisq df p
pers 40.37 1 2.1e-10
age 3.15 1 0.07575
male 8.88 1 0.00288
conservative 12.21 1 0.00047
GLOBAL 47.17 4 1.4e-09
chisq df p
pers 35.276 1 2.9e-09
academics 10.776 1 0.001
medinc 0.712 1 0.399
manufact 6.477 1 0.011
GLOBAL 37.669 4 1.3e-07
chisq df p
pers 45.88 1 1.3e-11
airport_dist 33.83 1 6.0e-09
tourism 25.00 1 5.7e-07
healthcare 1.81 1 0.18
popdens 47.05 1 6.9e-12
GLOBAL 101.93 5 < 2e-16
chisq df p
pers 34.785 1 3.7e-09
age 1.678 1 0.1953
male 6.300 1 0.0121
conservative 15.885 1 6.7e-05
academics 10.398 1 0.0013
medinc 0.941 1 0.3321
manufact 7.793 1 0.0052
airport_dist 29.969 1 4.4e-08
tourism 19.239 1 1.2e-05
healthcare 0.718 1 0.3968
popdens 34.662 1 3.9e-09
onset_prev 52.981 1 3.4e-13
slope_prev 59.230 1 1.4e-14
GLOBAL 121.454 13 < 2e-16
chisq df p
pers 12.4 1 0.00043
GLOBAL 12.4 1 0.00043
chisq df p
pers 12.03 1 0.00052
age 2.36 1 0.12437
male 6.84 1 0.00890
conservative 10.00 1 0.00157
GLOBAL 26.40 4 2.6e-05
chisq df p
pers 10.018 1 0.0016
academics 8.193 1 0.0042
medinc 0.203 1 0.6524
manufact 5.864 1 0.0155
GLOBAL 26.466 4 2.5e-05
chisq df p
pers 8.91 1 0.0028
airport_dist 35.75 1 2.2e-09
tourism 24.41 1 7.8e-07
healthcare 1.13 1 0.2872
popdens 63.76 1 1.4e-15
GLOBAL 112.08 5 < 2e-16
chisq df p
pers 8.922 1 0.00282
age 1.096 1 0.29514
male 5.384 1 0.02032
conservative 14.733 1 0.00012
academics 8.988 1 0.00272
medinc 0.515 1 0.47281
manufact 8.175 1 0.00425
airport_dist 32.147 1 1.4e-08
tourism 18.978 1 1.3e-05
healthcare 0.414 1 0.51984
popdens 36.422 1 1.6e-09
onset_prev 49.691 1 1.8e-12
slope_prev 59.684 1 1.1e-14
GLOBAL 120.284 13 < 2e-16
chisq df p
pers 0.561 1 0.45
GLOBAL 0.561 1 0.45
chisq df p
pers 0.427 1 0.51355
age 2.983 1 0.08417
male 7.640 1 0.00571
conservative 10.147 1 0.00145
GLOBAL 18.922 4 0.00081
chisq df p
pers 0.0957 1 0.7570
academics 9.7760 1 0.0018
medinc 0.5531 1 0.4571
manufact 6.6345 1 0.0100
GLOBAL 17.1795 4 0.0018
chisq df p
pers 0.402 1 0.53
airport_dist 32.089 1 1.5e-08
tourism 26.039 1 3.3e-07
healthcare 1.454 1 0.23
popdens 59.669 1 1.1e-14
GLOBAL 102.636 5 < 2e-16
chisq df p
pers 1.28e-03 1 0.97144
age 1.39e+00 1 0.23869
male 5.95e+00 1 0.01473
conservative 1.48e+01 1 0.00012
academics 1.03e+01 1 0.00133
medinc 8.99e-01 1 0.34313
manufact 8.21e+00 1 0.00417
airport_dist 2.96e+01 1 5.2e-08
tourism 1.96e+01 1 9.6e-06
healthcare 5.16e-01 1 0.47244
popdens 3.61e+01 1 1.9e-09
onset_prev 5.21e+01 1 5.2e-13
slope_prev 6.00e+01 1 9.3e-15
GLOBAL 1.18e+02 13 < 2e-16
chisq df p
pers 16.4 1 5.1e-05
GLOBAL 16.4 1 5.1e-05
chisq df p
pers 15.94 1 6.5e-05
age 2.83 1 0.0925
male 7.42 1 0.0065
conservative 10.10 1 0.0015
GLOBAL 28.85 4 8.4e-06
chisq df p
pers 13.905 1 0.00019
academics 9.124 1 0.00252
medinc 0.395 1 0.52976
manufact 6.149 1 0.01315
GLOBAL 32.382 4 1.6e-06
chisq df p
pers 11.5 1 0.0007
airport_dist 33.8 1 6.0e-09
tourism 24.3 1 8.3e-07
healthcare 1.1 1 0.2943
popdens 61.5 1 4.4e-15
GLOBAL 116.8 5 < 2e-16
chisq df p
pers 11.805 1 0.00059
age 1.281 1 0.25765
male 5.737 1 0.01661
conservative 14.695 1 0.00013
academics 9.849 1 0.00170
medinc 0.717 1 0.39707
manufact 8.389 1 0.00378
airport_dist 30.178 1 3.9e-08
tourism 18.997 1 1.3e-05
healthcare 0.393 1 0.53092
popdens 35.993 1 2.0e-09
onset_prev 51.526 1 7.1e-13
slope_prev 60.946 1 5.9e-15
GLOBAL 122.473 13 < 2e-16
chisq df p
pers 8.41 1 0.0037
GLOBAL 8.41 1 0.0037
chisq df p
pers 8.65 1 0.00328
age 1.99 1 0.15812
male 7.00 1 0.00814
conservative 10.05 1 0.00152
GLOBAL 19.68 4 0.00058
chisq df p
pers 9.659 1 0.00188
academics 7.704 1 0.00551
medinc 0.124 1 0.72482
manufact 7.032 1 0.00801
GLOBAL 20.333 4 0.00043
chisq df p
pers 7.99 1 0.0047
airport_dist 34.83 1 3.6e-09
tourism 24.38 1 7.9e-07
healthcare 0.57 1 0.4502
popdens 51.22 1 8.2e-13
GLOBAL 97.26 5 < 2e-16
chisq df p
pers 9.841 1 0.0017
age 0.987 1 0.3204
male 5.645 1 0.0175
conservative 15.669 1 7.5e-05
academics 9.134 1 0.0025
medinc 0.439 1 0.5078
manufact 9.371 1 0.0022
airport_dist 30.646 1 3.1e-08
tourism 19.652 1 9.3e-06
healthcare 0.180 1 0.6712
popdens 35.728 1 2.3e-09
onset_prev 50.955 1 9.4e-13
slope_prev 60.718 1 6.6e-15
GLOBAL 122.278 13 < 2e-16Assumptions GER socdist adjustment levels
list_iterater(ger_list_results$ger_lm_socdist_mean, test = 'qq')
list_iterater(ger_list_results$ger_lm_socdist_mean, test = 'bp')
studentized Breusch-Pagan test
data: .
BP = 6.6073, df = 1, p-value = 0.01016
studentized Breusch-Pagan test
data: .
BP = 11.402, df = 4, p-value = 0.02239
studentized Breusch-Pagan test
data: .
BP = 38.391, df = 4, p-value = 9.306e-08
studentized Breusch-Pagan test
data: .
BP = 0.91771, df = 5, p-value = 0.9689
studentized Breusch-Pagan test
data: .
BP = 36.57, df = 13, p-value = 0.0004836
studentized Breusch-Pagan test
data: .
BP = 2.8103, df = 1, p-value = 0.09366
studentized Breusch-Pagan test
data: .
BP = 16.321, df = 4, p-value = 0.002617
studentized Breusch-Pagan test
data: .
BP = 43.588, df = 4, p-value = 7.812e-09
studentized Breusch-Pagan test
data: .
BP = 3.9413, df = 5, p-value = 0.5579
studentized Breusch-Pagan test
data: .
BP = 40.982, df = 13, p-value = 9.594e-05
studentized Breusch-Pagan test
data: .
BP = 3.9066, df = 1, p-value = 0.0481
studentized Breusch-Pagan test
data: .
BP = 14.173, df = 4, p-value = 0.006764
studentized Breusch-Pagan test
data: .
BP = 37.668, df = 4, p-value = 1.312e-07
studentized Breusch-Pagan test
data: .
BP = 1.6777, df = 5, p-value = 0.8917
studentized Breusch-Pagan test
data: .
BP = 36.106, df = 13, p-value = 0.0005712
studentized Breusch-Pagan test
data: .
BP = 6.5055, df = 1, p-value = 0.01075
studentized Breusch-Pagan test
data: .
BP = 15.626, df = 4, p-value = 0.003565
studentized Breusch-Pagan test
data: .
BP = 40.792, df = 4, p-value = 2.968e-08
studentized Breusch-Pagan test
data: .
BP = 1.6497, df = 5, p-value = 0.8952
studentized Breusch-Pagan test
data: .
BP = 39.059, df = 13, p-value = 0.0001956
studentized Breusch-Pagan test
data: .
BP = 2.3883, df = 1, p-value = 0.1222
studentized Breusch-Pagan test
data: .
BP = 14.994, df = 4, p-value = 0.004713
studentized Breusch-Pagan test
data: .
BP = 38.685, df = 4, p-value = 8.091e-08
studentized Breusch-Pagan test
data: .
BP = 2.6498, df = 5, p-value = 0.7538
studentized Breusch-Pagan test
data: .
BP = 38.358, df = 13, p-value = 0.000253Assumptions US socdist adjustment levels
list_iterater(us_list_results$us_lm_socdist_mean, test = 'qq')
list_iterater(us_list_results$us_lm_socdist_mean, test = 'bp')
studentized Breusch-Pagan test
data: .
BP = 11.773, df = 1, p-value = 0.0006008
studentized Breusch-Pagan test
data: .
BP = 11.552, df = 4, p-value = 0.02101
studentized Breusch-Pagan test
data: .
BP = 9.536, df = 4, p-value = 0.04901
studentized Breusch-Pagan test
data: .
BP = 37.618, df = 5, p-value = 4.501e-07
studentized Breusch-Pagan test
data: .
BP = 33.974, df = 13, p-value = 0.001215
studentized Breusch-Pagan test
data: .
BP = 6.7876, df = 1, p-value = 0.00918
studentized Breusch-Pagan test
data: .
BP = 10.068, df = 4, p-value = 0.03929
studentized Breusch-Pagan test
data: .
BP = 11.336, df = 4, p-value = 0.02303
studentized Breusch-Pagan test
data: .
BP = 49.661, df = 5, p-value = 1.626e-09
studentized Breusch-Pagan test
data: .
BP = 34.866, df = 13, p-value = 0.0008878
studentized Breusch-Pagan test
data: .
BP = 0.18699, df = 1, p-value = 0.6654
studentized Breusch-Pagan test
data: .
BP = 14.373, df = 4, p-value = 0.006195
studentized Breusch-Pagan test
data: .
BP = 10.332, df = 4, p-value = 0.03519
studentized Breusch-Pagan test
data: .
BP = 50.528, df = 5, p-value = 1.081e-09
studentized Breusch-Pagan test
data: .
BP = 34.641, df = 13, p-value = 0.0009611
studentized Breusch-Pagan test
data: .
BP = 14.662, df = 1, p-value = 0.0001286
studentized Breusch-Pagan test
data: .
BP = 12.049, df = 4, p-value = 0.01699
studentized Breusch-Pagan test
data: .
BP = 15.089, df = 4, p-value = 0.004519
studentized Breusch-Pagan test
data: .
BP = 54.746, df = 5, p-value = 1.472e-10
studentized Breusch-Pagan test
data: .
BP = 36.335, df = 13, p-value = 0.0005262
studentized Breusch-Pagan test
data: .
BP = 9.3516, df = 1, p-value = 0.002228
studentized Breusch-Pagan test
data: .
BP = 20.051, df = 4, p-value = 0.000488
studentized Breusch-Pagan test
data: .
BP = 11.455, df = 4, p-value = 0.0219
studentized Breusch-Pagan test
data: .
BP = 51.643, df = 5, p-value = 6.386e-10
studentized Breusch-Pagan test
data: .
BP = 36.846, df = 13, p-value = 0.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